Intelligent MPPT Control for PV-Wind in Hybrid Microgrid System Using ANN Optimized Hippopotamus Algorithm

The increasing demand for renewable energy integration in hybrid microgrids necessitates intelligent control strategies to optimize power generation. Accurately tracking the Maximum Power Point (MPP) under fluctuating environmental conditions remains a significant challenge in hybrid photovoltaic (PV)-wind energy systems. To address this, the paper introduces an innovative MPPT method that employs an Artificial Neural Network (ANN) optimized using the Hippopotamus Algorithm (HA). This approach is specifically designed to enhance power extraction efficiency within a hybrid PV-wind configuration integrated into a microgrid environment. The proposed HA-ANN MPPT technique exhibits superior tracking efficiency, reduced steady-state oscillations, and faster convergence compared to conventional methods. Simulation results demonstrate that the HA-ANN MPPT improves power extraction efficiency under dynamic weather conditions while minimizing energy losses. Furthermore, the hybrid microgrid's overall performance, stability, and reliability are significantly enhanced. This research highlights the effectiveness of bio-inspired optimization algorithms in renewable energy applications and their potential to revolutionize MPPT strategies in hybrid microgrids. The proposed HA-ANN MPPT offers a promising solution for maximizing power utilization, ensuring sustainable energy management, and supporting the global transition toward renewable energy-based smart grids.